Hi! I'm a PhD student in the Machine Learning Department at CMU where I am fortunate to be advised by Pradeep Ravikumar and Andrej Risteski. I graduated from CMU with degrees in Computer Science and Statistics & Machine Learning. Before entering grad school I spent two years as a software engineer at Google NYC where I worked on Search Infrastructure and Machine Intelligence.
I'm broadly interested in theoretical foundations of machine learning; in particular, I focus on robustness, representation learning, and generalization under distribution shift. I work to develop formal models which allow for principled analyses of these tasks, with the eventual goal of devising new approaches to solving failure modes of existing methods.
Research/Mentorship Opportunities: I am always happy to discuss new research directions; if you're a student at CMU interested in working on domain adaptation / generalization, representation learning, or robust ML more generally, reach out to me! I am also available to give advice and feedback for those who are applying to undergraduate or graduate programs in computer science. If you are applying for a PhD in CMU SCS, I encourage you to sign up to receive feedback through the Graduate Application Support Program.
You can reach me at [firstname] at cmu.edu
[CV] [Semantic Scholar] [Google Scholar]
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
[arXiv]
Iterative Feature Matching: Toward Provable Domain Generalization
with Logarithmic Environments
NeurIPS 2022
Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski
[arXiv]
Analyzing and Improving the Optimization Landscape of
Noise-Contrastive Estimation
ICLR 2022
Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
[arXiv]
[blog post]
Deep Attentive Variational Inference
ICLR 2022
Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski
[OpenReview]
An Online Learning Approach to Interpolation and Extrapolation
in Domain Generalization
AISTATS 2022
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
[arXiv]
The Risks of Invariant Risk Minimization
ICLR 2021
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
[arXiv]
[poster]
[CMU AI Seminar Talk]
[2 minute spotlight presentation]
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
ICML 2020
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter
[arXiv]
[code (.zip)]
[blog post]
[virtual poster/presentation]
Certified Adversarial Robustness via Randomized Smoothing
ICML 2019
Jeremy Cohen, Elan Rosenfeld, Zico Kolter
[arXiv]
[code]
[ICML talk]
[Zico's Simons talk]
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
NeurIPS 2022 Workshop: Has It Trained Yet?
Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari, Oncel Tuzel, Fartash Faghri
[arXiv]
Self-Reflective Variational Autoencoder
ICLR 2021 Workshop: Hardware Aware Efficient Training
Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski
[arXiv]
[poster]
[short presentation]
Human-Usable Password Schemas: Beyond Information-Theoretic Security
CMU Senior Thesis
Awarded “Exemplary Senior Honors Thesis”
Elan Rosenfeld, Santosh Vempala, Manuel Blum
[arXiv]
[poster]